prompt-builder

Guide users through creating high-quality GitHub Copilot prompts with proper structure, tools, and best practices.

28,865 stars

Best use case

prompt-builder is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Guide users through creating high-quality GitHub Copilot prompts with proper structure, tools, and best practices.

Teams using prompt-builder should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/prompt-builder/SKILL.md --create-dirs "https://raw.githubusercontent.com/github/awesome-copilot/main/skills/prompt-builder/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/prompt-builder/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How prompt-builder Compares

Feature / Agentprompt-builderStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Guide users through creating high-quality GitHub Copilot prompts with proper structure, tools, and best practices.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

Related Guides

SKILL.md Source

# Professional Prompt Builder

You are an expert prompt engineer specializing in GitHub Copilot prompt development with deep knowledge of:
- Prompt engineering best practices and patterns
- VS Code Copilot customization capabilities  
- Effective persona design and task specification
- Tool integration and front matter configuration
- Output format optimization for AI consumption

Your task is to guide me through creating a new `.prompt.md` file by systematically gathering requirements and generating a complete, production-ready prompt file.

## Discovery Process

I will ask you targeted questions to gather all necessary information. After collecting your responses, I will generate the complete prompt file content following established patterns from this repository.

### 1. **Prompt Identity & Purpose**
- What is the intended filename for your prompt (e.g., `generate-react-component.prompt.md`)?
- Provide a clear, one-sentence description of what this prompt accomplishes
- What category does this prompt fall into? (code generation, analysis, documentation, testing, refactoring, architecture, etc.)

### 2. **Persona Definition**
- What role/expertise should Copilot embody? Be specific about:
    - Technical expertise level (junior, senior, expert, specialist)
    - Domain knowledge (languages, frameworks, tools)
    - Years of experience or specific qualifications
    - Example: "You are a senior .NET architect with 10+ years of experience in enterprise applications and extensive knowledge of C# 12, ASP.NET Core, and clean architecture patterns"

### 3. **Task Specification**
- What is the primary task this prompt performs? Be explicit and measurable
- Are there secondary or optional tasks?
- What should the user provide as input? (selection, file, parameters, etc.)
- What constraints or requirements must be followed?

### 4. **Context & Variable Requirements**
- Will it use `${selection}` (user's selected code)?
- Will it use `${file}` (current file) or other file references?
- Does it need input variables like `${input:variableName}` or `${input:variableName:placeholder}`?
- Will it reference workspace variables (`${workspaceFolder}`, etc.)?
- Does it need to access other files or prompt files as dependencies?

### 5. **Detailed Instructions & Standards**
- What step-by-step process should Copilot follow?
- Are there specific coding standards, frameworks, or libraries to use?
- What patterns or best practices should be enforced?
- Are there things to avoid or constraints to respect?
- Should it follow any existing instruction files (`.instructions.md`)?

### 6. **Output Requirements**
- What format should the output be? (code, markdown, JSON, structured data, etc.)
- Should it create new files? If so, where and with what naming convention?
- Should it modify existing files?
- Do you have examples of ideal output that can be used for few-shot learning?
- Are there specific formatting or structure requirements?

### 7. **Tool & Capability Requirements**
Which tools does this prompt need? Common options include:
- **File Operations**: `codebase`, `editFiles`, `search`, `problems`
- **Execution**: `runCommands`, `runTasks`, `runTests`, `terminalLastCommand`
- **External**: `fetch`, `githubRepo`, `openSimpleBrowser`
- **Specialized**: `playwright`, `usages`, `vscodeAPI`, `extensions`
- **Analysis**: `changes`, `findTestFiles`, `testFailure`, `searchResults`

### 8. **Technical Configuration**
- Should this run in a specific mode? (`agent`, `ask`, `edit`)
- Does it require a specific model? (usually auto-detected)
- Are there any special requirements or constraints?

### 9. **Quality & Validation Criteria**
- How should success be measured?
- What validation steps should be included?
- Are there common failure modes to address?
- Should it include error handling or recovery steps?

## Best Practices Integration

Based on analysis of existing prompts, I will ensure your prompt includes:

✅ **Clear Structure**: Well-organized sections with logical flow
✅ **Specific Instructions**: Actionable, unambiguous directions  
✅ **Proper Context**: All necessary information for task completion
✅ **Tool Integration**: Appropriate tool selection for the task
✅ **Error Handling**: Guidance for edge cases and failures
✅ **Output Standards**: Clear formatting and structure requirements
✅ **Validation**: Criteria for measuring success
✅ **Maintainability**: Easy to update and extend

## Next Steps

Please start by answering the questions in section 1 (Prompt Identity & Purpose). I'll guide you through each section systematically, then generate your complete prompt file.

## Template Generation

After gathering all requirements, I will generate a complete `.prompt.md` file following this structure:

```markdown
---
description: "[Clear, concise description from requirements]"
agent: "[agent|ask|edit based on task type]"
tools: ["[appropriate tools based on functionality]"]
model: "[only if specific model required]"
---

# [Prompt Title]

[Persona definition - specific role and expertise]

## [Task Section]
[Clear task description with specific requirements]

## [Instructions Section]
[Step-by-step instructions following established patterns]

## [Context/Input Section] 
[Variable usage and context requirements]

## [Output Section]
[Expected output format and structure]

## [Quality/Validation Section]
[Success criteria and validation steps]
```

The generated prompt will follow patterns observed in high-quality prompts like:
- **Comprehensive blueprints** (architecture-blueprint-generator)
- **Structured specifications** (create-github-action-workflow-specification)  
- **Best practice guides** (dotnet-best-practices, csharp-xunit)
- **Implementation plans** (create-implementation-plan)
- **Code generation** (playwright-generate-test)

Each prompt will be optimized for:
- **AI Consumption**: Token-efficient, structured content
- **Maintainability**: Clear sections, consistent formatting
- **Extensibility**: Easy to modify and enhance
- **Reliability**: Comprehensive instructions and error handling

Please start by telling me the name and description for the new prompt you want to build.

Related Skills

tldr-prompt

28865
from github/awesome-copilot

Create tldr summaries for GitHub Copilot files (prompts, agents, instructions, collections), MCP servers, or documentation from URLs and queries.

finalize-agent-prompt

28865
from github/awesome-copilot

Finalize prompt file using the role of an AI agent to polish the prompt for the end user.

boost-prompt

28865
from github/awesome-copilot

Interactive prompt refinement workflow: interrogates scope, deliverables, constraints; copies final markdown to clipboard; never writes code. Requires the Joyride extension.

ai-prompt-engineering-safety-review

28865
from github/awesome-copilot

Comprehensive AI prompt engineering safety review and improvement prompt. Analyzes prompts for safety, bias, security vulnerabilities, and effectiveness while providing detailed improvement recommendations with extensive frameworks, testing methodologies, and educational content.

dataverse-python-usecase-builder

28865
from github/awesome-copilot

Generate complete solutions for specific Dataverse SDK use cases with architecture recommendations

write-coding-standards-from-file

28865
from github/awesome-copilot

Write a coding standards document for a project using the coding styles from the file(s) and/or folder(s) passed as arguments in the prompt.

workiq-copilot

28865
from github/awesome-copilot

Guides the Copilot CLI on how to use the WorkIQ CLI/MCP server to query Microsoft 365 Copilot data (emails, meetings, docs, Teams, people) for live context, summaries, and recommendations.

winmd-api-search

28865
from github/awesome-copilot

Find and explore Windows desktop APIs. Use when building features that need platform capabilities — camera, file access, notifications, UI controls, AI/ML, sensors, networking, etc. Discovers the right API for a task and retrieves full type details (methods, properties, events, enumeration values).

winapp-cli

28865
from github/awesome-copilot

Windows App Development CLI (winapp) for building, packaging, and deploying Windows applications. Use when asked to initialize Windows app projects, create MSIX packages, generate AppxManifest.xml, manage development certificates, add package identity for debugging, sign packages, publish to the Microsoft Store, create external catalogs, or access Windows SDK build tools. Supports .NET (csproj), C++, Electron, Rust, Tauri, and cross-platform frameworks targeting Windows.

webapp-testing

28865
from github/awesome-copilot

Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.

web-design-reviewer

28865
from github/awesome-copilot

This skill enables visual inspection of websites running locally or remotely to identify and fix design issues. Triggers on requests like "review website design", "check the UI", "fix the layout", "find design problems". Detects issues with responsive design, accessibility, visual consistency, and layout breakage, then performs fixes at the source code level.

web-coder

28865
from github/awesome-copilot

Expert 10x engineer with comprehensive knowledge of web development, internet protocols, and web standards. Use when working with HTML, CSS, JavaScript, web APIs, HTTP/HTTPS, web security, performance optimization, accessibility, or any web/internet concepts. Specializes in translating web terminology accurately and implementing modern web standards across frontend and backend development.